Kernel Function Tuning for Single-Layer Neural Networks

0493061 - ÚI 2019 RIV SG eng J - Journal Article
Vidnerová, Petra - Neruda, Roman
Kernel Function Tuning for Single-Layer Neural Networks.
International Journal of Machine Learning and Computing. Roč. 8, č. 4 (2018), s. 354-360. ISSN 2010-3700
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : radial basis function networks * shallow neural networks * kernel methods * hyper-parameter tuning
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=79&id=831

This paper describes an unified learning framework for kernel networks with one hidden layer, including models like radial basis function networks and regularization networks. The learning procedure consists of meta-parameter tuning wrapping the standard parameter optimization part. Several variants of learning are described and tested on various classification and regression problems. It is shown that meta-learning can improve the performance of models for the price of higher time complexity. © 2018, International Association of Computer Science and Information Technology.
Permanent Link: http://hdl.handle.net/11104/0286524